According to IDC, by 2020, organizations able to analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity benefits over their less analytically oriented peers.
You’ll notice, IDC’s statement doesn’t say some data. Or a few pieces of data. It says all relevant data. Easier said than done in today’s environment, as massive amounts of data is created by connected devices and in more places than ever. The Internet of Things (IoT) is a great example—from data created by high speed powerboats that go up to 200 mph to extremely time sensitive data created by machines on manufacturing floors.
To get a piece of that $430 billion pie requires new technical capabilities to supplement existing data and analytics’ strategies; mainly the ability to capture, store and analyze data in the place where it is actively created. Also required is the ability to query billions of data records instantly and run hyper-distributed analytics to deliver the insights and experiences customers, business partners and employees expect. Whether you are in the business of racing or manufacturing – speed is critical.
This is exactly why Cisco is collaborating with industry leaders like IBM —to create capabilities that bring analytics from the cloud to the network’s edge to help organizations take advantage of all of its data assets. Cisco’s part of that is Edge Analytics Fabric (EAF), an open architecture platform that captures, stores, and analyzes data where it is actively created.
To better understand EAF, take the example of a digital manufacturer. Keeping machines running optimally is an essential part of running a successful facility and can be a big – and costly – challenge. Each machine creates large amounts of data every day that tell us about the health that machine. By embedding streaming analytics software into a Cisco Industrial Ethernet 4000 switch attached to the machine, we are able to analyze the vibration patterns on machine spindles in real-time. It can pick up ultrasonic machine vibrations beyond human perception and analyze the data quicker than humanly possible. If machine vibrations exceed the normal threshold, an alert is created so an employee can take immediate action.
In addition, the collection of raw machine data on the factory floor allows operations to collect historical information for analysis of the overall process over longer periods of time. Then it will send only data defined as essential to the Watson IoT platform in the cloud, where Watson’s cognitive and business analytics capabilities allow it to learn from this data and adjust its algorithms for optimal machine performance and provide enterprise wide views. Now, plant maintenance is able to be conducted more reliably and at the ideal time for cost effectiveness and productivity.
Having the right strategy and technical capabilities in place means more than just accessing and analyzing data. It’s about making it personal by putting it into the exact context to act on it for your business.
There is never a better time to get your piece of that $430 billion worth of increased productivity. Don’t be left behind.
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I love the Grabber $430B; Was not it always – How can you make Customer research stuff, evaluate stuff and buy staff faster? How can you reduce waste on the production floor? How can you improve your customer relationships by preemptively managing your product defect in the field? Just do it; Call it Big Data or Small Data, Business enterprises do not care, only Technology Vendors Do…..
Excellent article and thanks for the EAF white paper link. I agree that with the right analytic tool, makes a big difference.
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